Abstract:
Today, "Live Facial Expression Detection" is a highly common and crucial work. This approach can be used by a lot of businesses to comprehend the feelings of customers after a product introduction. It is also used to determine whether or not the employees are content with the amenities provided by the business. The potential for this discipline to advance human-machine cooperation is enormous. As a result, there is growing interest in precisely reading human expression. The visible representation of an individual's affective state, cognitive activity, intention, personality, and psychopathology is their facial expression, which also serves as a means of communication in interpersonal interactions. It has been researched for a very long time and has made progress in recent decades. Despite significant advancements, it is still challenging to accurately identify facial expressions because of their complexity and variety. There are numerous techniques that can identify facial expressions. Using HTML5, CSS3, Bootstrap, and JavaScript, we developed a system for identifying facial expressions for our thesis project. We used the face-API js JavaScript API and obtained 99.28% accuracy for our system.
